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Paul, Michael J Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Dr. Paul develops methods for analyzing and understanding data. His background is in machine learning, statistical modeling and natural language processing, which he uses to solve problems in health informatics and epidemiology, using new and transformative sources of data. For example, his research has shown how to analyze social media to monitor disease outbreaks and track trends in population health.

keywords

  • machine learning, natural language processing, statistical modeling, data mining, text mining, text analysis, social media analysis, sentiment analysis, computational social science, computational epidemiology, health informatics, medical informatics, digital humanities

Publications

selected publications

Teaching

courses taught

  • INFO 2301 - Quantitative Reasoning for Information Science
    Primary Instructor - Spring 2018 / Spring 2019
    Introduces methods for quantifying and analyzing different types of data, covering foundational concepts in discrete mathematics, probability, and predictive modeling, along with complementary computational skills to apply these concepts to real problems. Covers counting and combinatorics, logic, set theory, introductory probability, common probability distributions, regression, and model validation. Requires demonstrated proficiency with introductory computer programming.
  • INFO 4604 - Mastery in Information Science: Applied Machine Learning
    Primary Instructor - Fall 2018
    Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Same as INFO 5604.
  • INFO 4871 - Special Topics
    Primary Instructor - Fall 2019
    Special topics.
  • INFO 5604 - Mastery in Information Science: Applied Machine Learning
    Primary Instructor - Fall 2018
    Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Same as INFO 4604.
  • INFO 5871 - Special Topics
    Primary Instructor - Fall 2019
    Topics will vary by semester.

Background

International Activities

global connections related to teaching and scholarly work (in recent years)

Other Profiles

Github

  • michaeljpaul